Regression analysis in R: backward selection

preview_player
Показать описание
Selecting variables for a linear model is dicey. Let's get started! If this vid helps you, please help me a tiny bit by mashing that 'like' button. For more #rstats joy, crush that 'subscribe' button!
  1. Equitable Equations
Рекомендации по теме
Regression analysis in 0:22:42

Regression analysis in R: backward selection

Variable selection procedures 0:22:09

Variable selection procedures in R: Forward, backward, stepwise, and best-subsets regression (2020)

multiple linear regression 0:19:50

multiple linear regression in R backward elimination homework video 42 machine learning

multiple linear regression 0:08:25

multiple linear regression in R backward elimination homework solution video43 machine learning

RStudio Basics | 0:18:04

RStudio Basics | Fitting a Multiple Linear Regression Model using Backward Elimination Method

Multiple Regression Backward 0:07:06

Multiple Regression Backward Elimination R Markdown Code

Multiple Regression in 0:07:43

Multiple Regression in R, Step by Step!!!

Multiple Regression using 0:12:11

Multiple Regression using Backward Elimination Method in SPSS

Run Stepwise Regression 0:08:45

Run Stepwise Regression in R

5.28: Variable selection 0:04:25

5.28: Variable selection by forward and backward step-wise regressions

Forward, backward, and 0:26:02

Forward, backward, and hierarchical binary logistic regression in SPSS

R Stats: Multiple 0:18:47

R Stats: Multiple Regression - Variable Selection

Multiple linear regression 0:09:48

Multiple linear regression with interaction in R

Regression - 12: 0:10:27

Regression - 12: Backward Elimination method to build Multiple linear regression model

Multiple regression: how 0:10:46

Multiple regression: how to select variables for your model

Tutorial | Implementation 0:19:05

Tutorial | Implementation Of stepwise regression in R | Best Practice | How to check linearity

Stepwise forward and 0:05:27

Stepwise forward and backward elimination regression in Matlab

Linear Regression_____023 Multiple 0:17:52

Linear Regression_____023 Multiple Linear Regression in R Backward Elimination HOMEWORK !

Variable Selection Methods 0:12:05

Variable Selection Methods for Model Building (Forward, Backward and Stepwise Regression) in R

Linear Regression_____024 Multiple 0:07:35

Linear Regression_____024 Multiple Linear Regression in R Backward Elimination Homework Solution

Stepwise Regression In 0:25:15

Stepwise Regression In R software - Part I

Variable/Feature Selection using 0:11:55

Variable/Feature Selection using Forward Selection in R with Interpretation| Linear Regression

Stepwise Regression 0:11:30

Stepwise Regression

Statistics using R 0:08:20

Statistics using R programming - Stepwise regression for variable selection using R

Комментарии
Автор

Great video. Please also make a video on selection in logistic regression. And also how we can automate the removal of variables based on a criteria.

Ammarsays
Автор

Really interesting topic. Great video, really well explained!

Автор

Thanks for the great video. Really well done.

evanspencer
Автор

I heard you mention this wouldn't be devoted to inference, but wondered if you could help me understand the pitfalls of stepwise elimination as its summarized in this sentence I found: "If you remove the insignificant terms and then refit, the inference results (p-values) would not include the "effect" of the previous selection". I can't wrap my head around what this means practically and what the implications might be. Any corrections or thoughts? Thank you, I really enjoy your videos!

brazilfootball
Автор

My humble request you to make videos regarding ggplot2 with tools geom_ point, geom_bar, geom_line, pie chart, geom_area and others geom relates charts with one single dataset with every charts all the syntax. It will be useful as a beginner. Thank you so much for your great effort. ❤❤❤❤

DrGKumar-mebx
Автор

Please give a link to the data set (performance.csv).

louiseweschler
Автор

Thank you for the very helpful video, and I have a question: Why do we need linear regression when there are machine learning methods?

ooda